Towards a Well-being-Oriented Framework for Urban Digital Twins
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Journal Article
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Elsevier BV
Abstract
Urban well-being is gaining prominence as a critical pillar of sustainable development practice and urban planning; however, digital twin technology continues to focus predominantly on physical infrastructure. This paper introduces an exploratory conceptual framework for incorporating urban well-being indicators into urban digital twin platforms, utilizing New Zealand's Living Standards Framework (LSF) and adopting a policy-oriented approach to selecting well-being indicators. Through consultation with experts and a literature review, we identified six policy-relevant proxies: carbon emissions, drinking water quality, road fatalities, crime rates, work commute times, and internet access, which reflect the environmental, social, and economic dimensions of well-being. Historical data from 2017 to 2023 was operationalised in a Python-based analytical dashboard, which generates descriptive statistics, benchmarks, correlations, and Autoregressive Integrated Moving Average (ARIMA) forecasts. The study also assessed the technical feasibility of urban well-being indicators using publicly available open-source digital twin platforms such as Eclipse Ditto and FIWARE. The results indicate that integration is technically feasible; however, they are constrained by schema incompatibilities, limited native analytics capabilities, and questions of scalability regarding how proxies relate to urban well-being. As a proof-of-concept study, it explored how digital twin technology could be reshaped to support holistic, citizen-oriented objectives for well-being and complement participatory and multi-criteria approaches.Description
Keywords
44 Human Society, 33 Built Environment and Design, 3304 Urban and Regional Planning, 1205 Urban and Regional Planning, 1604 Human Geography, Urban & Regional Planning, 4406 Human geography, 4407 Policy and administration, Digital twin technology, Well-being indicators, Urban planning, Living standards framework, Real-time data integration, Predictive modelling
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Cities, ISSN: 0264-2751 (Print), Elsevier BV, 169, 106579-106579. doi: 10.1016/j.cities.2025.106579
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© 2025 The Author(s). Published by Elsevier Ltd. Creative Commons. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.